String Algorithms and Pattern Matching Questions

Covers algorithmic techniques and practical skills for solving string problems and pattern matching tasks. Core algorithm knowledge includes substring search and pattern matching algorithms such as Knuth Morris Pratt, Rabin Karp, Boyer Moore, Z algorithm, Aho Corasick for multiple pattern matching, and rolling hash methods. Data structures and suffix structures are important, including tries, suffix arrays, suffix trees, and suffix automata, together with longest common prefix arrays and related construction techniques. Also includes dynamic programming approaches for string problems such as edit distance and longest common subsequence, palindrome and anagram detection methods, and regular expression concepts and engine behavior. Emphasizes algorithmic complexity analysis, time and space trade offs, memory and streaming constraints, and optimization strategies for very long inputs and high throughput text processing. Practical considerations include parsing and string manipulation idioms in common languages, Unicode and character encoding issues, edge case handling, test case design for strings, and real world applications such as log analysis, text search, and data transformation.

HardTechnical

60 practiced

Implement wildcard pattern matching with '?' matching any single character and '*' matching any sequence (including empty). Signature: bool matches(const string& text, const string& pattern) in C++. Optimize for time and space, discuss worst-case complexity, and explain how to avoid pathological backtracking behavior.

MediumTechnical

54 practiced

Given strings S and T, implement minimumWindow(S, T) in C++ to return the smallest substring of S that contains all characters of T (including multiplicities). Aim for O(|S| + |T|) time using a sliding-window technique and explain corner cases and tests you would run.

EasyTechnical

48 practiced

Write a Python function find_all_occurrences(pattern: str, text: str) -> List[int] that returns all starting indices where pattern appears in text using the naive O(n*m) algorithm. Explain time and space complexity and show output for pattern='aba' and text='ababa'. Mention edge cases such as empty pattern and pattern longer than text.

MediumTechnical

47 practiced

Implement the Boyer-Moore algorithm in Python combining both bad-character and good-suffix heuristics. Provide function boyer_moore_search(text, pattern) that returns all match positions. Explain preprocessing for both heuristics and analyze worst-case behavior and possible optimizations.

EasyTechnical

55 practiced

Explain the trie (prefix tree) data structure: describe insert, search, and delete operations and their time/space complexity in terms of string length L and alphabet size σ. Discuss memory trade-offs, use of compressed tries (radix trees), and scenarios where a trie is preferable over a hash map for string keys.

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